A biologically inspired spiking model of visual processing for image feature detection

To enable fast reliable feature matching or tracking in scenes, features need to be discrete and meaningful, and hence edge or corner features, commonly called interest points are often used for this purpose. Experimental research has illustrated that biological vision systems use neuronal circuits to extract particular features such as edges or corners from visual scenes. Inspired by this biological behaviour, this paper proposes a biologically inspired spiking neural network for the purpose of image feature extraction. Standard digital images are processed and converted to spikes in a manner similar to the processing that transforms light into spikes in the retina. Using a hierarchical spiking network, various types of biologically inspired receptive fields are used to extract progressively complex image features. The performance of the network is assessed by examining the repeatability of extracted features with visual results presented using both synthetic and real images

Eye Corner Detection

Detection of corners of the eye is a good research topic. It plays an important role in multiple tasks performed in the field of Computer Vision. It also plays a key role in biometric systems. In this the- sis, initially, the existing corner detection methods are discussed. Using Hough transform line, circle and ellipse were found out in the given image. The proposed work includes, finding the eye region in the given face image using Template Matching method. Later on, we fit a rectangle to the matched eye region. And then, we find out the corners of the rectangle and approximate them to be the corners of the eye